A Split-Merge DP-means Algorithm to Avoid Local Minima

We present an extension of the DP-means algorithm, a hard-clustering approximation of nonparametric Bayesian models. Although a recent work [6] reports that the DP-means can converge to a local minimum, the condition for the DP-means to converge to a local minimum is still unknown. This paper demonstrates one reason the DP-means converges to a local minimum: the DP-means cannot assign the optimal number of clusters when many data points exist within small distances. As a first attempt to avoid the local minimum, we propose an extension of the DP-means by the split-merge technique. The proposed algorithm splits clusters when a cluster has many data points to assign the number of clusters near to optimal. The experimental results with multiple datasets show the robustness of the proposed algorithm.